A novel ensemble ARIMA‐LSTM approach for evaluating COVID‐19 cases and future outbreak preparedness
The proposed ARIMA-LSTM hybrid model outperforms ARIMA, GRU, LSTM, Prophet, and the ARIMA-ANN hybrid model when evaluating using metrics like MAPE, symmetric mean absolute percentage error, and median absolute percentage error across all countries analyzed. These findings have the potential to significantly improve preparedness and response efforts by public health authorities, allowing for more efficient resource allocation and targeted interventions.
Authors
- Shobhit Agrawal
- Somit Jain
- Kathiravan Srinivasan (ORCID: https://orcid.org/0000-0002-9352-0237)
- Eshaan Mohapatra
Institutions
- Vellore Institute of Technology University (IN)
Publication Details
- Journal
- Health care science
- Published
- 2024-12-01
- DOI
- https://doi.org/10.1002/hcs2.123
- Citations
- 11
- Primary Topic
- Stock Market Forecasting Methods
- Type
- article
- Field-Weighted Citation Impact
- 5.40